Storage, retrieval, and processing of various types of data permits businesses to engineer new business plans and/or adjust existing business plans for optimum performance. For example, a wireless telephone company may manage a data warehouse to store existing and/or new subscriber information. If the wireless company processes such collected data, growth trends may be discovered that suggest new market opportunities, and/or overburdened markets in need of service equipment rehabilitation and/or addition. Analysis of business data stored in a data warehouse may allow the business to convert such data into business intelligence, learn more about their customers, and/or make various management decisions based on empirical information rather than heuristics.
Non-business related organizations may also analyze warehoused data to meet various engineered objectives. For example, governments and/or municipalities may study population growth and/or population movement to better prepare city infrastructure and/or services for increased use. Similarly, such population trend analysis may illustrate that allocated budgets may be redirected from lesser needed city projects to higher prioritized projects in neighborhoods with substantial population growth rates.
A data warehouse may maintain copies of data for subsequent analysis. Data may include population data, financial data, business data, and/or behavioral data, such as cable television subscriptions, home buying behavior, and/or broadcast programming consumption. The data warehouse may be stored in a variety of ways, including in a relational database, a multidimensional database, a flat file, a hierarchical database, an object database, etc. Reports generated from the data warehouse are typically created to expose specific metrics important to the business, government entity, and/or other group(s). The reports typically consume a finite amount of processing and memory resources, which may result in diminished data warehouse performance as the size of the stored data increases.
Moreover, if multiple clients seek reports from a particular data warehouse at overlapping times, the decreased performance capabilities may result in unsatisfactory wait times for the clients, and/or an inability to run queries in a manner responsive to quickly changing client demands. For example, some data warehouses may require multiple hours of processing time to generate a report for a client. If the client subsequently chooses an alternate set of parameters for the report, then the client must wait a significant amount of time for the next opportunity to run a query on the overburdened data warehouse. At other times, depending on the processing loads of the data warehouse, the processing time may be lower, thereby making it difficult for a data warehouse manager to efficiently allocate and/or manage data warehouse processing time for multiple clients.